These simulations used prior specifications that involved a large prior SS, i.e. \(\sigma^2 \sim IG(1,100)\). While this prior is quite large given the true variance in the data-generating process, \(\sigma^2 = 10\), it appears in the well-separated case to result in improved clustering performance under the DPMM.
## `summarise()` has grouped output by 'Model', 'Scenario', 'SM'. You can override
## using the `.groups` argument.
| Model | SM | 3wellsep_30 | 3wellsep_100 | 3wellsep_300 |
|---|---|---|---|---|
| conjDEE | noSM | 0.95 (0.93,0.97) | 0.8 (0.71,0.85) | 0.69 (0.56,0.88) |
| conjDEE | withSM | 0.94 (0.92,0.96) | 0.94 (0.88,0.96) | 0.85 (0.69,0.98) |
| conjDEV | noSM | 0.99 (0.98,1) | 1 (0.86,1) | 1 (0.86,1) |
| conjDEV | withSM | 0.98 (0.96,0.99) | 0.99 (0.97,0.99) | 0.99 (0.98,1) |
## `summarise()` has grouped output by 'Model', 'Scenario', 'SM'. You can override
## using the `.groups` argument.
| Model | SM | 3wellsep_30 | 3wellsep_100 | 3wellsep_300 |
|---|---|---|---|---|
| conjDEE | noSM | 0.07 (0.05,0.09) | 0.03 (0.02,0.04) | 0.01 (0.01,0.02) |
| conjDEE | withSM | 0.07 (0.05,0.09) | 0.02 (0.01,0.03) | 0.01 (0,0.01) |
| conjDEV | noSM | 0.08 (0.06,0.09) | 0.03 (0.02,0.04) | 0.01 (0.01,0.01) |
| conjDEV | withSM | 0.07 (0.06,0.09) | 0.02 (0.02,0.03) | 0.01 (0,0.01) |
## `summarise()` has grouped output by 'Model', 'Scenario', 'SM'. You can override
## using the `.groups` argument.
| Model | SM | 3wellsep_30 | 3wellsep_100 | 3wellsep_300 |
|---|---|---|---|---|
| conjDEE | noSM | 3 (3,3) | 4 (4,5) | 5 (3,7) |
| conjDEE | withSM | 3 (3,3) | 3 (3,3) | 3 (3,3) |
| conjDEV | noSM | 3 (3,3) | 3 (3,4) | 3 (3,4) |
| conjDEV | withSM | 3 (3,3) | 3 (3,3) | 3 (3,3) |
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] -1
## [2,] 1
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 3.097688
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] FALSE
##
## $sm_iter
## [1] 0
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] 0
## [2,] 0
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 18.20804
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] FALSE
##
## $sm_iter
## [1] 0
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] 0
## [2,] 0
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 3.104484
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] FALSE
##
## $sm_iter
## [1] 0
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] -1
## [2,] 1
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 7.94319
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] TRUE
##
## $sm_iter
## [1] 10
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] 0
## [2,] 0
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 2.646533
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] TRUE
##
## $sm_iter
## [1] 10
## $S
## [1] 12000
##
## $alpha
## [1] 1
##
## $a
## [1] 1
##
## $b
## [1] 100
##
## $mu0
## [,1]
## [1,] 0
## [2,] 0
##
## $k_init
## [1] 1
##
## $init_method
## [1] "kmeans"
##
## $d
## [1] 1
##
## $f
## [1] 1
##
## $g
## [1] 1
##
## $h
## [1] 25
##
## $r
## [1] 5.259726
##
## $mod_type
## [1] "conjDEE"
##
## $split_merge
## [1] TRUE
##
## $sm_iter
## [1] 10